{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-14T08:47:36.974399900Z",
     "start_time": "2023-06-14T08:47:35.601877155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<contextlib.ExitStack at 0x7fba8c1b9790>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import torch.backends.cudnn as cudnn\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "from PIL import Image\n",
    "from tempfile import TemporaryDirectory\n",
    "\n",
    "cudnn.benchmark = True\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        #transforms.Grayscale(num_output_channels=3),\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        #transforms.Grayscale(num_output_channels=3),\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = 'dummy_data/hymenoptera_data'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T09:58:51.387679798Z",
     "start_time": "2023-06-14T09:58:51.326990348Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Display image for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T09:58:54.566749654Z",
     "start_time": "2023-06-14T09:58:54.013958557Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    # Create a temporary directory to save training checkpoints\n",
    "    with TemporaryDirectory() as tempdir:\n",
    "        best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')\n",
    "\n",
    "        torch.save(model.state_dict(), best_model_params_path)\n",
    "        best_acc = 0.0\n",
    "\n",
    "        for epoch in range(num_epochs):\n",
    "            print(f'Epoch {epoch+1}/{num_epochs}')\n",
    "            print('-' * 10)\n",
    "\n",
    "            # Each epoch has a training and validation phase\n",
    "            for phase in ['train', 'val']:\n",
    "                if phase == 'train':\n",
    "                    model.train()  # Set model to training mode\n",
    "                else:\n",
    "                    model.eval()   # Set model to evaluate mode\n",
    "\n",
    "                running_loss = 0.0\n",
    "                running_corrects = 0\n",
    "\n",
    "                # Iterate over data.\n",
    "                for inputs, labels in dataloaders[phase]:\n",
    "                    inputs = inputs.to(device)\n",
    "                    labels = labels.to(device)\n",
    "\n",
    "                    # zero the parameter gradients\n",
    "                    optimizer.zero_grad()\n",
    "\n",
    "                    # forward\n",
    "                    # track history if only in train\n",
    "                    with torch.set_grad_enabled(phase == 'train'):\n",
    "                        outputs = model(inputs)\n",
    "                        _, preds = torch.max(outputs, 1)\n",
    "                        loss = criterion(outputs, labels)\n",
    "\n",
    "                        # backward + optimize only if in training phase\n",
    "                        if phase == 'train':\n",
    "                            loss.backward()\n",
    "                            optimizer.step()\n",
    "\n",
    "                    # statistics\n",
    "                    running_loss += loss.item() * inputs.size(0)\n",
    "                    running_corrects += torch.sum(preds == labels.data)\n",
    "                if phase == 'train':\n",
    "                    scheduler.step()\n",
    "\n",
    "                epoch_loss = running_loss / dataset_sizes[phase]\n",
    "                epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "                print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
    "\n",
    "                # deep copy the model\n",
    "                if phase == 'val' and epoch_acc > best_acc:\n",
    "                    best_acc = epoch_acc\n",
    "                    torch.save(model.state_dict(), best_model_params_path)\n",
    "\n",
    "            print()\n",
    "\n",
    "        time_elapsed = time.time() - since\n",
    "        print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
    "        print(f'Best val Acc: {best_acc:4f}')\n",
    "\n",
    "        # load best model weights\n",
    "        model.load_state_dict(torch.load(best_model_params_path))\n",
    "    return model"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T08:47:37.377701012Z",
     "start_time": "2023-06-14T08:47:37.368468185Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title(f'predicted: {class_names[preds[j]]}')\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T08:47:37.434592225Z",
     "start_time": "2023-06-14T08:47:37.380742071Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet152-f82ba261.pth\" to /home/tadeusz/.cache/torch/hub/checkpoints/resnet152-f82ba261.pth\n",
      "100%|██████████| 230M/230M [02:01<00:00, 1.99MB/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": "2048"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_152 = models.resnet152(weights=models.ResNet152_Weights.DEFAULT)\n",
    "num_ftrs = model_152.fc.in_features\n",
    "num_ftrs"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T08:55:19.428830414Z",
     "start_time": "2023-06-14T08:53:16.486204881Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "model_152.fc = nn.Linear(num_ftrs,2)\n",
    "model_152.to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer_152 = optim.SGD(model_152.parameters(), lr=0.001, momentum=0.9)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_152, step_size=7, gamma=0.1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T09:06:14.495541670Z",
     "start_time": "2023-06-14T09:06:13.421061020Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "----------\n",
      "train Loss: 0.2879 Acc: 0.9057\n",
      "val Loss: 0.2072 Acc: 0.9739\n",
      "\n",
      "Epoch 2/25\n",
      "----------\n",
      "train Loss: 0.2961 Acc: 0.8893\n",
      "val Loss: 0.2175 Acc: 0.9608\n",
      "\n",
      "Epoch 3/25\n",
      "----------\n",
      "train Loss: 0.2479 Acc: 0.9221\n",
      "val Loss: 0.2117 Acc: 0.9673\n",
      "\n",
      "Epoch 4/25\n",
      "----------\n",
      "train Loss: 0.2812 Acc: 0.8852\n",
      "val Loss: 0.2067 Acc: 0.9739\n",
      "\n",
      "Epoch 5/25\n",
      "----------\n",
      "train Loss: 0.2885 Acc: 0.8975\n",
      "val Loss: 0.1937 Acc: 0.9804\n",
      "\n",
      "Epoch 6/25\n",
      "----------\n",
      "train Loss: 0.2853 Acc: 0.8852\n",
      "val Loss: 0.2223 Acc: 0.9542\n",
      "\n",
      "Epoch 7/25\n",
      "----------\n",
      "train Loss: 0.2656 Acc: 0.9098\n",
      "val Loss: 0.2223 Acc: 0.9608\n",
      "\n",
      "Epoch 8/25\n",
      "----------\n",
      "train Loss: 0.2570 Acc: 0.8975\n",
      "val Loss: 0.2076 Acc: 0.9673\n",
      "\n",
      "Epoch 9/25\n",
      "----------\n",
      "train Loss: 0.3078 Acc: 0.8730\n",
      "val Loss: 0.2186 Acc: 0.9673\n",
      "\n",
      "Epoch 10/25\n",
      "----------\n",
      "train Loss: 0.2822 Acc: 0.8975\n",
      "val Loss: 0.2018 Acc: 0.9804\n",
      "\n",
      "Epoch 11/25\n",
      "----------\n",
      "train Loss: 0.2586 Acc: 0.9098\n",
      "val Loss: 0.2050 Acc: 0.9673\n",
      "\n",
      "Epoch 12/25\n",
      "----------\n",
      "train Loss: 0.3307 Acc: 0.8402\n",
      "val Loss: 0.1954 Acc: 0.9739\n",
      "\n",
      "Epoch 13/25\n",
      "----------\n",
      "train Loss: 0.2534 Acc: 0.9262\n",
      "val Loss: 0.2181 Acc: 0.9673\n",
      "\n",
      "Epoch 14/25\n",
      "----------\n",
      "train Loss: 0.2898 Acc: 0.8934\n",
      "val Loss: 0.2122 Acc: 0.9673\n",
      "\n",
      "Epoch 15/25\n",
      "----------\n",
      "train Loss: 0.2840 Acc: 0.9016\n",
      "val Loss: 0.2140 Acc: 0.9804\n",
      "\n",
      "Epoch 16/25\n",
      "----------\n",
      "train Loss: 0.3202 Acc: 0.8893\n",
      "val Loss: 0.2003 Acc: 0.9739\n",
      "\n",
      "Epoch 17/25\n",
      "----------\n",
      "train Loss: 0.2803 Acc: 0.8893\n",
      "val Loss: 0.1865 Acc: 0.9804\n",
      "\n",
      "Epoch 18/25\n",
      "----------\n",
      "train Loss: 0.2797 Acc: 0.9057\n",
      "val Loss: 0.2071 Acc: 0.9673\n",
      "\n",
      "Epoch 19/25\n",
      "----------\n",
      "train Loss: 0.2935 Acc: 0.8770\n",
      "val Loss: 0.2223 Acc: 0.9542\n",
      "\n",
      "Epoch 20/25\n",
      "----------\n",
      "train Loss: 0.3110 Acc: 0.8648\n",
      "val Loss: 0.2446 Acc: 0.9412\n",
      "\n",
      "Epoch 21/25\n",
      "----------\n",
      "train Loss: 0.2684 Acc: 0.9057\n",
      "val Loss: 0.2019 Acc: 0.9804\n",
      "\n",
      "Epoch 22/25\n",
      "----------\n",
      "train Loss: 0.2573 Acc: 0.9098\n",
      "val Loss: 0.2094 Acc: 0.9608\n",
      "\n",
      "Epoch 23/25\n",
      "----------\n",
      "train Loss: 0.2911 Acc: 0.8770\n",
      "val Loss: 0.2097 Acc: 0.9804\n",
      "\n",
      "Epoch 24/25\n",
      "----------\n",
      "train Loss: 0.2804 Acc: 0.8811\n",
      "val Loss: 0.3018 Acc: 0.9085\n",
      "\n",
      "Epoch 25/25\n",
      "----------\n",
      "train Loss: 0.3493 Acc: 0.8566\n",
      "val Loss: 0.2250 Acc: 0.9673\n",
      "\n",
      "Training complete in 2m 41s\n",
      "Best val Acc: 0.980392\n"
     ]
    }
   ],
   "source": [
    "model_152 = train_model(model_152, criterion, optimizer_152, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T09:38:00.041997283Z",
     "start_time": "2023-06-14T09:35:18.873145382Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "torch.save(model_152, 'dummy_data/resnet152_bees_ants_model')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T09:40:56.469019523Z",
     "start_time": "2023-06-14T09:40:56.128250909Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# looking at the accuracy if only 1 channel is given to the network"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dummy_data/hymenoptera_data/huh/huh.txt/loool\n"
     ]
    }
   ],
   "source": [
    "for x in ['train', 'val']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-14T09:29:37.256023990Z",
     "start_time": "2023-06-14T09:29:37.250031104Z"
    }
   }
  }
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